Overview

Dataset statistics

Number of variables12
Number of observations217
Missing cells20
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.7 KiB
Average record size in memory263.0 B

Variable types

Categorical3
Numeric9

Alerts

Country Name has a high cardinality: 217 distinct valuesHigh cardinality
Country Code has a high cardinality: 217 distinct valuesHigh cardinality
Expected Years of School is highly overall correlated with Fraction of Children Under 5 Not Stunted and 7 other fieldsHigh correlation
Fraction of Children Under 5 Not Stunted is highly overall correlated with Expected Years of School and 8 other fieldsHigh correlation
Harmonized Test Scores is highly overall correlated with Expected Years of School and 7 other fieldsHigh correlation
Human Capital Index (0-1) is highly overall correlated with Expected Years of School and 7 other fieldsHigh correlation
Human Capital Index Lower Bound (0-1) is highly overall correlated with Expected Years of School and 7 other fieldsHigh correlation
Human Capital Index Upper Bound (0-1) is highly overall correlated with Expected Years of School and 7 other fieldsHigh correlation
Learning Adjusted Years of School is highly overall correlated with Expected Years of School and 8 other fieldsHigh correlation
Probability of Survival to Age 5 is highly overall correlated with Expected Years of School and 7 other fieldsHigh correlation
Survival Rate from Age 15-60 is highly overall correlated with Expected Years of School and 7 other fieldsHigh correlation
Continent is highly overall correlated with Fraction of Children Under 5 Not Stunted and 1 other fieldsHigh correlation
Country Name is uniformly distributedUniform
Country Code is uniformly distributedUniform
Country Name has unique valuesUnique
Country Code has unique valuesUnique

Reproduction

Analysis started2023-05-24 14:27:25.775175
Analysis finished2023-05-24 14:27:41.696226
Duration15.92 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Country Name
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct217
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
Afghanistan
 
1
Pakistan
 
1
Nepal
 
1
Netherlands
 
1
New Caledonia
 
1
Other values (212)
212 

Length

Max length30
Median length22
Mean length9.6635945
Min length4

Characters and Unicode

Total characters2097
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique217 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAmerican Samoa
5th rowAndorra

Common Values

ValueCountFrequency (%)
Afghanistan 1
 
0.5%
Pakistan 1
 
0.5%
Nepal 1
 
0.5%
Netherlands 1
 
0.5%
New Caledonia 1
 
0.5%
New Zealand 1
 
0.5%
Nicaragua 1
 
0.5%
Niger 1
 
0.5%
Nigeria 1
 
0.5%
North Macedonia 1
 
0.5%
Other values (207) 207
95.4%

Length

2023-05-24T09:27:41.882228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
islands 9
 
2.8%
and 8
 
2.5%
rep 7
 
2.2%
republic 6
 
1.9%
st 4
 
1.3%
new 3
 
0.9%
guinea 3
 
0.9%
arab 3
 
0.9%
united 3
 
0.9%
the 3
 
0.9%
Other values (255) 267
84.5%

Most occurring characters

ValueCountFrequency (%)
a 297
 
14.2%
n 162
 
7.7%
i 162
 
7.7%
e 144
 
6.9%
r 116
 
5.5%
o 104
 
5.0%
99
 
4.7%
s 75
 
3.6%
u 74
 
3.5%
t 74
 
3.5%
Other values (48) 790
37.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1644
78.4%
Uppercase Letter 314
 
15.0%
Space Separator 99
 
4.7%
Other Punctuation 32
 
1.5%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 297
18.1%
n 162
9.9%
i 162
9.9%
e 144
 
8.8%
r 116
 
7.1%
o 104
 
6.3%
s 75
 
4.6%
u 74
 
4.5%
t 74
 
4.5%
l 74
 
4.5%
Other values (16) 362
22.0%
Uppercase Letter
ValueCountFrequency (%)
S 35
 
11.1%
M 25
 
8.0%
C 25
 
8.0%
B 23
 
7.3%
R 22
 
7.0%
A 21
 
6.7%
I 20
 
6.4%
G 18
 
5.7%
T 16
 
5.1%
N 14
 
4.5%
Other values (15) 95
30.3%
Other Punctuation
ValueCountFrequency (%)
. 17
53.1%
, 13
40.6%
' 2
 
6.2%
Space Separator
ValueCountFrequency (%)
99
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1958
93.4%
Common 139
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 297
15.2%
n 162
 
8.3%
i 162
 
8.3%
e 144
 
7.4%
r 116
 
5.9%
o 104
 
5.3%
s 75
 
3.8%
u 74
 
3.8%
t 74
 
3.8%
l 74
 
3.8%
Other values (41) 676
34.5%
Common
ValueCountFrequency (%)
99
71.2%
. 17
 
12.2%
, 13
 
9.4%
) 3
 
2.2%
( 3
 
2.2%
' 2
 
1.4%
- 2
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2097
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 297
 
14.2%
n 162
 
7.7%
i 162
 
7.7%
e 144
 
6.9%
r 116
 
5.5%
o 104
 
5.0%
99
 
4.7%
s 75
 
3.6%
u 74
 
3.5%
t 74
 
3.5%
Other values (48) 790
37.7%

Country Code
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct217
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
AFG
 
1
PAK
 
1
NPL
 
1
NLD
 
1
NCL
 
1
Other values (212)
212 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters651
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique217 ?
Unique (%)100.0%

Sample

1st rowAFG
2nd rowALB
3rd rowDZA
4th rowASM
5th rowAND

Common Values

ValueCountFrequency (%)
AFG 1
 
0.5%
PAK 1
 
0.5%
NPL 1
 
0.5%
NLD 1
 
0.5%
NCL 1
 
0.5%
NZL 1
 
0.5%
NIC 1
 
0.5%
NER 1
 
0.5%
NGA 1
 
0.5%
MKD 1
 
0.5%
Other values (207) 207
95.4%

Length

2023-05-24T09:27:42.053751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
afg 1
 
0.5%
aze 1
 
0.5%
bwa 1
 
0.5%
dza 1
 
0.5%
asm 1
 
0.5%
and 1
 
0.5%
ago 1
 
0.5%
atg 1
 
0.5%
arg 1
 
0.5%
arm 1
 
0.5%
Other values (207) 207
95.4%

Most occurring characters

ValueCountFrequency (%)
R 49
 
7.5%
A 48
 
7.4%
M 47
 
7.2%
N 44
 
6.8%
S 37
 
5.7%
L 35
 
5.4%
B 35
 
5.4%
G 33
 
5.1%
T 31
 
4.8%
C 30
 
4.6%
Other values (16) 262
40.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 651
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 49
 
7.5%
A 48
 
7.4%
M 47
 
7.2%
N 44
 
6.8%
S 37
 
5.7%
L 35
 
5.4%
B 35
 
5.4%
G 33
 
5.1%
T 31
 
4.8%
C 30
 
4.6%
Other values (16) 262
40.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 651
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 49
 
7.5%
A 48
 
7.4%
M 47
 
7.2%
N 44
 
6.8%
S 37
 
5.7%
L 35
 
5.4%
B 35
 
5.4%
G 33
 
5.1%
T 31
 
4.8%
C 30
 
4.6%
Other values (16) 262
40.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 49
 
7.5%
A 48
 
7.4%
M 47
 
7.2%
N 44
 
6.8%
S 37
 
5.7%
L 35
 
5.4%
B 35
 
5.4%
G 33
 
5.1%
T 31
 
4.8%
C 30
 
4.6%
Other values (16) 262
40.2%

Expected Years of School
Real number (ℝ)

Distinct179
Distinct (%)83.3%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean11.338767
Minimum4.1569886
Maximum13.936425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-05-24T09:27:42.247472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4.1569886
5-th percentile7.0112355
Q110.132842
median11.989869
Q313.000679
95-th percentile13.729422
Maximum13.936425
Range9.7794366
Interquartile range (IQR)2.8678379

Descriptive statistics

Standard deviation2.2031716
Coefficient of variation (CV)0.19430434
Kurtosis0.56052172
Mean11.338767
Median Absolute Deviation (MAD)1.216758
Skewness-1.1011313
Sum2437.8348
Variance4.8539649
MonotonicityNot monotonic
2023-05-24T09:27:42.441323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.98986897 14
 
6.5%
13.20662699 8
 
3.7%
8.53717408 8
 
3.7%
11.28257002 5
 
2.3%
11.92672605 4
 
1.8%
12.47876358 3
 
1.4%
8.901890755 1
 
0.5%
9.373867989 1
 
0.5%
13.69681358 1
 
0.5%
10.75874996 1
 
0.5%
Other values (169) 169
77.9%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
4.156988621 1
0.5%
4.569999695 1
0.5%
4.677145958 1
0.5%
5.242938995 1
0.5%
5.310591698 1
0.5%
5.498250008 1
0.5%
6.42372179 1
0.5%
6.823250771 1
0.5%
6.852043152 1
0.5%
6.931927681 1
0.5%
ValueCountFrequency (%)
13.93642521 1
0.5%
13.92090034 1
0.5%
13.9008379 1
0.5%
13.9005394 1
0.5%
13.87876892 1
0.5%
13.85829639 1
0.5%
13.8328495 1
0.5%
13.8077774 1
0.5%
13.80265808 1
0.5%
13.76294804 1
0.5%

Fraction of Children Under 5 Not Stunted
Real number (ℝ)

Distinct104
Distinct (%)48.4%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean0.80315036
Minimum0.458
Maximum0.97514689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-05-24T09:27:42.627222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.458
5-th percentile0.60776262
Q10.73459211
median0.82489729
Q30.91900003
95-th percentile0.92386734
Maximum0.97514689
Range0.51714689
Interquartile range (IQR)0.18440792

Descriptive statistics

Standard deviation0.10894836
Coefficient of variation (CV)0.13565126
Kurtosis-0.14054155
Mean0.80315036
Median Absolute Deviation (MAD)0.09410274
Skewness-0.65291412
Sum172.67733
Variance0.011869745
MonotonicityNot monotonic
2023-05-24T09:27:42.836633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9229360223 40
18.4%
0.8357511428 24
 
11.1%
0.7743813601 21
 
9.7%
0.7037758331 12
 
5.5%
0.7615223101 12
 
5.5%
0.8773048917 6
 
2.8%
0.7619999647 2
 
0.9%
0.9190000296 2
 
0.9%
0.8274065852 1
 
0.5%
0.5151730776 1
 
0.5%
Other values (94) 94
43.3%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
0.4580000043 1
0.5%
0.5054570436 1
0.5%
0.5151730776 1
0.5%
0.5328638554 1
0.5%
0.5359053016 1
0.5%
0.5440000296 1
0.5%
0.5730823278 1
0.5%
0.5770815611 1
0.5%
0.5837296247 1
0.5%
0.5920000076 1
0.5%
ValueCountFrequency (%)
0.9751468897 1
0.5%
0.9508137703 1
0.5%
0.9505164623 1
0.5%
0.9440251589 1
0.5%
0.9401782751 1
0.5%
0.9400208592 1
0.5%
0.9399999976 1
0.5%
0.9358964562 1
0.5%
0.9300000072 1
0.5%
0.9287617207 1
0.5%

Harmonized Test Scores
Real number (ℝ)

Distinct179
Distinct (%)83.3%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean422.79114
Minimum304.92224
Maximum575.27216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-05-24T09:27:43.054096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum304.92224
5-th percentile337.14695
Q1379.76813
median414.02185
Q3466.00778
95-th percentile520.49222
Maximum575.27216
Range270.34991
Interquartile range (IQR)86.239655

Descriptive statistics

Standard deviation59.270365
Coefficient of variation (CV)0.14018828
Kurtosis-0.59469478
Mean422.79114
Median Absolute Deviation (MAD)40.226874
Skewness0.3489381
Sum90900.096
Variance3512.9761
MonotonicityNot monotonic
2023-05-24T09:27:43.247484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
414.021849 14
 
6.5%
491.6000163 8
 
3.7%
373.7949749 8
 
3.7%
397.0653817 5
 
2.3%
429.4615624 4
 
1.8%
410.8271044 3
 
1.4%
354.7587891 1
 
0.5%
338.6565552 1
 
0.5%
519.7484131 1
 
0.5%
391.990448 1
 
0.5%
Other values (169) 169
77.9%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
304.9222412 1
0.5%
307.2757874 1
0.5%
307.3649597 1
0.5%
309.0249634 1
0.5%
310.1971436 1
0.5%
315.875946 1
0.5%
321.3270264 1
0.5%
325.9654846 1
0.5%
331.7459412 1
0.5%
333.1113281 1
0.5%
ValueCountFrequency (%)
575.2721558 1
0.5%
560.6135254 1
0.5%
548.6314087 1
0.5%
543.2060547 1
0.5%
537.7234497 1
0.5%
537.2097168 1
0.5%
533.9979858 1
0.5%
533.7076416 1
0.5%
530.0858154 1
0.5%
521.3285522 1
0.5%

Human Capital Index (0-1)
Real number (ℝ)

Distinct178
Distinct (%)82.8%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean0.56154822
Minimum0.29163191
Maximum0.87912571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-05-24T09:27:43.448485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.29163191
5-th percentile0.36957051
Q10.45218547
median0.56602362
Q30.6520381
95-th percentile0.77892513
Maximum0.87912571
Range0.58749381
Interquartile range (IQR)0.19985263

Descriptive statistics

Standard deviation0.13181903
Coefficient of variation (CV)0.23474214
Kurtosis-0.82187514
Mean0.56154822
Median Absolute Deviation (MAD)0.10841117
Skewness0.10316989
Sum120.73287
Variance0.017376257
MonotonicityNot monotonic
2023-05-24T09:27:43.625925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5660236227 14
 
6.5%
0.4100678317 8
 
3.7%
0.7107685499 8
 
3.7%
0.5288617206 5
 
2.3%
0.5896201885 4
 
1.8%
0.5812434918 3
 
1.4%
0.5041163564 2
 
0.9%
0.6081162691 1
 
0.5%
0.7899150252 1
 
0.5%
0.7759324312 1
 
0.5%
Other values (168) 168
77.4%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
0.2916319072 1
0.5%
0.2997827828 1
0.5%
0.3065208495 1
0.5%
0.3156851828 1
0.5%
0.3182695806 1
0.5%
0.3190142214 1
0.5%
0.3606098294 1
0.5%
0.3621166646 1
0.5%
0.3624053895 1
0.5%
0.3627021015 1
0.5%
ValueCountFrequency (%)
0.8791257143 1
0.5%
0.8127565384 1
0.5%
0.8047142625 1
0.5%
0.798776269 1
0.5%
0.7975201011 1
0.5%
0.7960010171 1
0.5%
0.7956715822 1
0.5%
0.7953571081 1
0.5%
0.7925990224 1
0.5%
0.7899150252 1
0.5%

Human Capital Index Lower Bound (0-1)
Real number (ℝ)

Distinct179
Distinct (%)83.3%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean0.54736389
Minimum0.25583741
Maximum0.87167621
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-05-24T09:27:43.828953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.25583741
5-th percentile0.34518881
Q10.43249787
median0.55288588
Q30.64078695
95-th percentile0.77062805
Maximum0.87167621
Range0.6158388
Interquartile range (IQR)0.20828907

Descriptive statistics

Standard deviation0.13614518
Coefficient of variation (CV)0.24872884
Kurtosis-0.82990441
Mean0.54736389
Median Absolute Deviation (MAD)0.11146813
Skewness0.07998926
Sum117.68324
Variance0.018535511
MonotonicityNot monotonic
2023-05-24T09:27:44.024493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5528858837 14
 
6.5%
0.7018482807 8
 
3.7%
0.3896917435 8
 
3.7%
0.5107854754 5
 
2.3%
0.5773801512 4
 
1.8%
0.5694872803 3
 
1.4%
0.3871486187 1
 
0.5%
0.391759634 1
 
0.5%
0.7680107951 1
 
0.5%
0.5000671744 1
 
0.5%
Other values (169) 169
77.9%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
0.2558374107 1
0.5%
0.2673529983 1
0.5%
0.2816317081 1
0.5%
0.2948280275 1
0.5%
0.3031578958 1
0.5%
0.3068152368 1
0.5%
0.3298636973 1
0.5%
0.3345715106 1
0.5%
0.3430505693 1
0.5%
0.3438639939 1
0.5%
ValueCountFrequency (%)
0.8716762066 1
0.5%
0.8024086952 1
0.5%
0.7967959046 1
0.5%
0.7895967364 1
0.5%
0.7895455956 1
0.5%
0.7883390784 1
0.5%
0.7870250344 1
0.5%
0.785323143 1
0.5%
0.7835327387 1
0.5%
0.7802541852 1
0.5%

Human Capital Index Upper Bound (0-1)
Real number (ℝ)

Distinct179
Distinct (%)83.3%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean0.57411618
Minimum0.31715703
Maximum0.8862676
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-05-24T09:27:44.232450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.31715703
5-th percentile0.38629589
Q10.46788339
median0.57762468
Q30.66078115
95-th percentile0.78947477
Maximum0.8862676
Range0.56911057
Interquartile range (IQR)0.19289775

Descriptive statistics

Standard deviation0.12884851
Coefficient of variation (CV)0.22442933
Kurtosis-0.8125459
Mean0.57411618
Median Absolute Deviation (MAD)0.10603333
Skewness0.11680897
Sum123.43498
Variance0.016601938
MonotonicityNot monotonic
2023-05-24T09:27:44.434598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.577624677 14
 
6.5%
0.7195550723 8
 
3.7%
0.4273459231 8
 
3.7%
0.5438641076 5
 
2.3%
0.6008849772 4
 
1.8%
0.5916607579 3
 
1.4%
0.4126172662 1
 
0.5%
0.4194494784 1
 
0.5%
0.7834054232 1
 
0.5%
0.5155310631 1
 
0.5%
Other values (169) 169
77.9%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
0.3171570301 1
0.5%
0.3207479119 1
0.5%
0.3287631571 1
0.5%
0.3316626847 1
0.5%
0.3326170146 1
0.5%
0.3344749808 1
0.5%
0.3773842156 1
0.5%
0.3792309761 1
0.5%
0.381459713 1
0.5%
0.3849897981 1
0.5%
ValueCountFrequency (%)
0.8862676024 1
0.5%
0.8226003647 1
0.5%
0.8132536411 1
0.5%
0.8092198372 1
0.5%
0.8055559993 1
0.5%
0.8050231338 1
0.5%
0.8046728969 1
0.5%
0.8014585972 1
0.5%
0.8010845184 1
0.5%
0.7995548248 1
0.5%

Learning Adjusted Years of School
Real number (ℝ)

Distinct179
Distinct (%)83.3%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean7.8237651
Minimum2.2065024
Maximum12.81329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-05-24T09:27:44.804360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.2065024
5-th percentile4.2814998
Q16.065496
median7.9828228
Q39.5267134
95-th percentile11.538311
Maximum12.81329
Range10.606787
Interquartile range (IQR)3.4612174

Descriptive statistics

Standard deviation2.336469
Coefficient of variation (CV)0.29863742
Kurtosis-0.7112569
Mean7.8237651
Median Absolute Deviation (MAD)1.8127543
Skewness-0.11120583
Sum1682.1095
Variance5.4590876
MonotonicityNot monotonic
2023-05-24T09:27:44.998674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.982822795 14
 
6.5%
10.41701871 8
 
3.7%
5.147474647 8
 
3.7%
7.262641907 5
 
2.3%
8.290895099 4
 
1.8%
8.214824094 3
 
1.4%
5.052838326 1
 
0.5%
5.079235077 1
 
0.5%
11.39023495 1
 
0.5%
6.747723103 1
 
0.5%
Other values (169) 169
77.9%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
2.206502438 1
0.5%
2.51134634 1
0.5%
2.578393221 1
0.5%
2.682461977 1
0.5%
2.696154833 1
0.5%
2.830429077 1
0.5%
3.925771236 1
0.5%
4.030860424 1
0.5%
4.178761482 1
0.5%
4.231436729 1
0.5%
ValueCountFrequency (%)
12.81328964 1
0.5%
11.88678265 1
0.5%
11.73875237 1
0.5%
11.73514938 1
0.5%
11.72404575 1
0.5%
11.71931267 1
0.5%
11.68402767 1
0.5%
11.59479713 1
0.5%
11.58087444 1
0.5%
11.57381439 1
0.5%

Probability of Survival to Age 5
Real number (ℝ)

Distinct177
Distinct (%)82.3%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean0.97285254
Minimum0.88008636
Maximum0.99830461
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-05-24T09:27:45.209632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.88008636
5-th percentile0.91905131
Q10.96190351
median0.98143773
Q30.99286455
95-th percentile0.99703558
Maximum0.99830461
Range0.11821824
Interquartile range (IQR)0.030961037

Descriptive statistics

Standard deviation0.026529105
Coefficient of variation (CV)0.027269401
Kurtosis1.5112288
Mean0.97285254
Median Absolute Deviation (MAD)0.012976358
Skewness-1.4302191
Sum209.1633
Variance0.00070379344
MonotonicityNot monotonic
2023-05-24T09:27:45.431291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9814377333 14
 
6.5%
0.9380123861 8
 
3.7%
0.9952628872 8
 
3.7%
0.9749162069 5
 
2.3%
0.9799713713 4
 
1.8%
0.9913992286 3
 
1.4%
0.9853207403 3
 
1.4%
0.9820764065 1
 
0.5%
0.9817061424 1
 
0.5%
0.9163431525 1
 
0.5%
Other values (167) 167
77.0%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
0.8800863624 1
0.5%
0.8810218573 1
0.5%
0.8835216165 1
0.5%
0.8949325681 1
0.5%
0.8992216587 1
0.5%
0.901424706 1
0.5%
0.9022251964 1
0.5%
0.9070135355 1
0.5%
0.9118964672 1
0.5%
0.9163431525 1
0.5%
ValueCountFrequency (%)
0.9983046055 1
0.5%
0.9980381727 1
0.5%
0.9978567362 1
0.5%
0.9976169467 1
0.5%
0.9975751042 1
0.5%
0.997510314 1
0.5%
0.9974570274 1
0.5%
0.9974550009 1
0.5%
0.9973526597 1
0.5%
0.9972904921 1
0.5%

Survival Rate from Age 15-60
Real number (ℝ)

Distinct179
Distinct (%)83.3%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean0.84852478
Minimum0.52254379
Maximum0.96143442
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-05-24T09:27:45.621954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.52254379
5-th percentile0.69664082
Q10.80366707
median0.86278166
Q30.91040345
95-th percentile0.94894722
Maximum0.96143442
Range0.43889064
Interquartile range (IQR)0.10673638

Descriptive statistics

Standard deviation0.080088708
Coefficient of variation (CV)0.09438582
Kurtosis1.3041932
Mean0.84852478
Median Absolute Deviation (MAD)0.048400123
Skewness-1.0757036
Sum182.43283
Variance0.0064142011
MonotonicityNot monotonic
2023-05-24T09:27:45.798492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8627816627 14
 
6.5%
0.9104034503 8
 
3.7%
0.7499216912 8
 
3.7%
0.8455300927 5
 
2.3%
0.883096669 4
 
1.8%
0.8713662227 3
 
1.4%
0.7877408266 1
 
0.5%
0.8453097343 1
 
0.5%
0.9399597049 1
 
0.5%
0.8513867855 1
 
0.5%
Other values (169) 169
77.9%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
0.522543788 1
0.5%
0.5913954377 1
0.5%
0.603710413 1
0.5%
0.6311577559 1
0.5%
0.646125257 1
0.5%
0.6503993273 1
0.5%
0.6585401297 1
0.5%
0.6600455046 1
0.5%
0.6772227287 1
0.5%
0.680878818 1
0.5%
ValueCountFrequency (%)
0.9614344239 1
0.5%
0.9604228735 1
0.5%
0.9548193812 1
0.5%
0.9546135068 1
0.5%
0.9538422227 1
0.5%
0.9527933598 1
0.5%
0.952393055 1
0.5%
0.9520066381 1
0.5%
0.9510608315 1
0.5%
0.9503155947 1
0.5%

Continent
Categorical

Distinct6
Distinct (%)2.8%
Missing2
Missing (%)0.9%
Memory size13.6 KiB
Africa
54 
Asia
50 
Europe
47 
North America
33 
Oceania
19 

Length

Max length13
Median length7
Mean length7.0883721
Min length4

Characters and Unicode

Total characters1524
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsia
2nd rowEurope
3rd rowAfrica
4th rowOceania
5th rowEurope

Common Values

ValueCountFrequency (%)
Africa 54
24.9%
Asia 50
23.0%
Europe 47
21.7%
North America 33
15.2%
Oceania 19
 
8.8%
South America 12
 
5.5%
(Missing) 2
 
0.9%

Length

2023-05-24T09:27:45.962837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-24T09:27:46.161707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
africa 54
20.8%
asia 50
19.2%
europe 47
18.1%
america 45
17.3%
north 33
12.7%
oceania 19
 
7.3%
south 12
 
4.6%

Most occurring characters

ValueCountFrequency (%)
a 187
12.3%
r 179
11.7%
i 168
11.0%
A 149
9.8%
c 118
 
7.7%
e 111
 
7.3%
o 92
 
6.0%
u 59
 
3.9%
f 54
 
3.5%
s 50
 
3.3%
Other values (10) 357
23.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1219
80.0%
Uppercase Letter 260
 
17.1%
Space Separator 45
 
3.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 187
15.3%
r 179
14.7%
i 168
13.8%
c 118
9.7%
e 111
9.1%
o 92
7.5%
u 59
 
4.8%
f 54
 
4.4%
s 50
 
4.1%
p 47
 
3.9%
Other values (4) 154
12.6%
Uppercase Letter
ValueCountFrequency (%)
A 149
57.3%
E 47
 
18.1%
N 33
 
12.7%
O 19
 
7.3%
S 12
 
4.6%
Space Separator
ValueCountFrequency (%)
45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1479
97.0%
Common 45
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 187
12.6%
r 179
12.1%
i 168
11.4%
A 149
10.1%
c 118
 
8.0%
e 111
 
7.5%
o 92
 
6.2%
u 59
 
4.0%
f 54
 
3.7%
s 50
 
3.4%
Other values (9) 312
21.1%
Common
ValueCountFrequency (%)
45
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 187
12.3%
r 179
11.7%
i 168
11.0%
A 149
9.8%
c 118
 
7.7%
e 111
 
7.3%
o 92
 
6.0%
u 59
 
3.9%
f 54
 
3.5%
s 50
 
3.3%
Other values (10) 357
23.4%

Interactions

2023-05-24T09:27:39.196352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:27.171866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:28.672661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:30.192378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:31.618754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:33.391604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:34.941997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:36.393975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:37.770187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:39.341879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:27.359202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:28.855884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:30.346143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:31.781748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:33.561715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:35.104853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:36.546769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:37.929838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:39.510216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:27.520149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:29.037577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:30.517506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:32.161224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:33.732640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:35.266514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:36.692486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:38.083120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:39.663126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:27.683930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:29.191777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:30.671300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:32.371893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:33.903433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:35.426917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:36.837628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:38.243257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:39.808405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:27.843233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:29.353274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:30.842885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:32.549276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:34.081040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:35.588510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:36.992254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:38.411819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:39.953081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:28.004050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:29.528812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:31.005275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:32.728211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:34.257575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:35.753227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:37.161596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:38.581156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:40.090910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:28.173303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:29.682201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:31.170318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:32.906069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:34.427419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:35.907590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:37.332054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:38.762216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:40.406478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:28.358565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:29.852396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:31.314230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:33.059769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:34.612329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:36.063745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:37.477689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:38.912221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:40.559711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:28.520266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:30.030875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:31.490750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:33.220906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:34.789522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:36.223587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:37.621687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-24T09:27:39.067161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-05-24T09:27:46.323337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Expected Years of SchoolFraction of Children Under 5 Not StuntedHarmonized Test ScoresHuman Capital Index (0-1)Human Capital Index Lower Bound (0-1)Human Capital Index Upper Bound (0-1)Learning Adjusted Years of SchoolProbability of Survival to Age 5Survival Rate from Age 15-60Continent
Expected Years of School1.0000.6750.7910.9490.9490.9470.9560.8860.7630.427
Fraction of Children Under 5 Not Stunted0.6751.0000.5980.7080.7080.7050.6780.7370.5920.576
Harmonized Test Scores0.7910.5981.0000.9050.9000.9080.9140.8300.7060.412
Human Capital Index (0-1)0.9490.7080.9051.0000.9990.9990.9900.9370.8480.463
Human Capital Index Lower Bound (0-1)0.9490.7080.9000.9991.0000.9970.9880.9420.8520.488
Human Capital Index Upper Bound (0-1)0.9470.7050.9080.9990.9971.0000.9910.9320.8430.475
Learning Adjusted Years of School0.9560.6780.9140.9900.9880.9911.0000.9080.7950.506
Probability of Survival to Age 50.8860.7370.8300.9370.9420.9320.9081.0000.8580.433
Survival Rate from Age 15-600.7630.5920.7060.8480.8520.8430.7950.8581.0000.395
Continent0.4270.5760.4120.4630.4880.4750.5060.4330.3951.000

Missing values

2023-05-24T09:27:40.771673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-24T09:27:41.116925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-24T09:27:41.453093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Country NameCountry CodeExpected Years of SchoolFraction of Children Under 5 Not StuntedHarmonized Test ScoresHuman Capital Index (0-1)Human Capital Index Lower Bound (0-1)Human Capital Index Upper Bound (0-1)Learning Adjusted Years of SchoolProbability of Survival to Age 5Survival Rate from Age 15-60Continent
0AfghanistanAFG8.9018910.618072354.7587890.4002840.3871490.4126175.0528380.9377240.787741Asia
1AlbaniaALB12.8893810.886766434.1275940.6342510.6247260.6434788.9530180.9911770.929366Europe
2AlgeriaDZA11.8480350.883104374.0890810.5345560.5273110.5415107.0915530.9765180.909282Africa
3American SamoaASM11.2825700.761522397.0653820.5288620.5107850.5438647.2626420.9749160.845530Oceania
4AndorraAND13.2066270.922936491.6000160.7107690.7018480.71955510.4170190.9952630.910403Europe
5AngolaAGO8.1200660.624289325.9654850.3624050.3298640.3853754.2349780.9228350.729359Africa
6Antigua and BarbudaATG12.9675600.835751406.9974370.5957040.5840800.6074158.4444220.9935590.897208North America
7ArgentinaARG12.8795230.921000408.1726380.6021450.5916670.6117168.4113100.9900550.888269South America
8ArmeniaARM11.2798700.906011442.9694520.5789990.5692160.5885327.9946210.9876190.885819Asia
9ArubaABW11.9898690.835751414.0218490.5660240.5528860.5776257.9828230.9814380.862782North America
Country NameCountry CodeExpected Years of SchoolFraction of Children Under 5 Not StuntedHarmonized Test ScoresHuman Capital Index (0-1)Human Capital Index Lower Bound (0-1)Human Capital Index Upper Bound (0-1)Learning Adjusted Years of SchoolProbability of Survival to Age 5Survival Rate from Age 15-60Continent
207UruguayURY12.2105160.877305437.6966250.5987600.5891420.6080748.5512030.9924200.893718South America
208UzbekistanUZB12.0410290.891524474.0751340.6228060.6042980.6409639.1333640.9785540.866114Asia
209VanuatuVUT10.1101670.711042347.6858220.4546590.4355540.4704135.6242590.9735560.873641Oceania
210Venezuela, RBVEN12.4787640.877305410.8271040.5812430.5694870.5916618.2148240.9853210.871366South America
211VietnamVNM12.8594400.762000519.1002810.6899650.6711330.70823710.6805420.9793170.867067Asia
212Virgin Islands (U.S.)VIR11.9898690.835751414.0218490.5660240.5528860.5776257.9828230.9814380.862782North America
213West Bank and GazaPSE12.1982550.926040412.3177490.5799970.5671820.5910908.0472910.9797340.892102Asia
214Yemen, Rep.YEM8.1279370.535905321.3270260.3727840.3528500.3882514.1787610.9450410.803752Asia
215ZambiaZMB8.7889630.654127358.1404420.3969280.3813840.4103185.0362930.9421580.732388Africa
216ZimbabweZWE11.0545370.765030396.1388240.4668930.4444810.4882597.0066100.9537720.650399Africa